Prediction of storm transfers and annual loads with data-based mechanistic models using high-frequency data

Excess nutrients in surface waters, such as phosphorus
(P) from agriculture, result in poor water quality,
with adverse effects on ecological health and costs for remediation.
However, understanding and prediction of P transfers
in catchments have been limited by inadequate data and
over-parameterised models with high uncertainty. We show
that, with high temporal resolution data, we are able to identify simple dynamic models that capture the P load dynamics in three contrasting agricultural catchments in the UK.
For a flashy catchment, a linear, second-order (two pathways)
model for discharge gave high simulation efficiencies
for short-term storm sequences and was useful in highlighting
uncertainties in out-of-bank flows. A model with nonlinear
rainfall input was appropriate for predicting seasonal
or annual cumulative P loads where antecedent conditions
affected the catchment response. For second-order models,
the time constant for the fast pathway varied between 2 and
15 h for all three catchments and for both discharge and P,
confirming that high temporal resolution data are necessary
to capture the dynamic responses in small catchments (10–
50 km2/. The models led to a better understanding of the
dominant nutrient transfer modes, which will be helpful in
determining phosphorus transfers following changes in precipitation patterns in the future.